Fault Identification Technology of 66 kV Transmission Lines Based on Fault Feature Matrix and IPSO-WNN

Zhang, Qi, Wang, Minzhen, Yang, Yongsheng, Wang, Xinheng ORCID: 0000-0001-8771-8901, Qi, Entie and Li, Cheng (2023) Fault Identification Technology of 66 kV Transmission Lines Based on Fault Feature Matrix and IPSO-WNN. APPLIED SCIENCES-BASEL, 13 (2). p. 1220.

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Official URL: http://dx.doi.org/10.3390/app13021220

Abstract

<jats:p>Due to the barely resonant earthed system used in the transmission line, it is more challenging to identify faults at a 66 kV voltage level because of insufficient fault identification techniques. In this paper, a 66 kV transmission line fault identification method based on a fault characteristic matrix and an improved particle swarm optimization (IPSO)-wavelet neural network (WNN) is proposed to address the difficulties in extracting and detecting characteristic parameters. The maximum matrix of the dbN wavelet was used to determine its decomposition scale and construct the fault characteristic matrix based on the energy values of frequency bands. The decomposition scale of the dbN wavelet was determined by the modulus maximum matrix to ensure the integrity of fault information. The fault feature matrix was then constructed based on the energy values of frequency bands and the fault feature was accurately extracted. In this research, aiming at the problems such as slow convergence speed and a tendency to fall into local minima, the WNN algorithm is enhanced with the IPSO algorithm. This significantly increased the convergence speed of the identification model and its ability to discover the global optimal solution. The simulation results demonstrate that this method can effectively and accurately identify the fault type with high identification accuracy, quick identification, and robust adaptability. Under challenging working conditions, it is capable of accurately identifying the fault type of 66 kV transmission lines.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	66 kV transmission line, resonant grounding system, wavelet neural network, characteristic matrix
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	24 Apr 2023 14:21
Last Modified:	17 Mar 2024 16:06
DOI:	10.3390/app13021220
Open Access URL:	https://doi.org/10.3390/app13021220
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3169926